Papers with BrowseComp-Plus
AWARE: Agentic Knowledge Warehousing for Contextual Intelligence (2026.findings-acl)
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| Challenge: | Large language models excel in information seeking tasks, but their knowledge is limited in coverage and timeliness. |
| Approach: | They propose an agentic knowledge warehousing framework that transforms unstructured data into minimal, task-conditioned knowledge representations consumable by LLMs. |
| Outcome: | Experiments on GAIA, WebWalker, and BrowseComp-Plus show improvements over baselines. |
MemoBrain: Executive Memory as an Agentic Brain for Reasoning (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are inherently long-horizon, causing reasoning traces and tool artifacts to accumulate and strain the working context of large language models. |
| Approach: | They propose a model that constructs a dependency-aware memory over reasoning steps and captures salient intermediate states and their logical relations. |
| Outcome: | The proposed model prunes invalid steps, folds completed sub-trajectories, and preserves a compact, high-salience reasoning backbone under a fixed context budget. |
BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents (2026.acl-long)
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Zijian Chen, Xueguang Ma, Shengyao Zhuang, Ping Nie, Kai Zou, Sahel Sharifymoghaddam, Andrew Liu, Joshua Green, Kshama Patel, Ruoxi Meng, Mingyi Su, Yanxi Li, Haoran Hong, Xinyu Shi, Xuye Liu, Hosna Oyarhoseini, Nandan Thakur, Crystina Zhang, Luyu Gao, Wenhu Chen, Jimmy Lin
| Challenge: | Existing benchmarks for deep search agents rely on blackbox web search APIs . dynamic and opaque web APIs hinder reproducibility and fair comparisons - authors . |
| Approach: | They propose a benchmark that employs a fixed corpus for controlled retrieval for deep search agents. |
| Outcome: | The new benchmark shows that agents that combine large language models with retrieval tools excel at complex, reasoning-intensive queries. |
Rerank Before You Reason: Analyzing Reranking Tradeoffs through Effective Token Cost in Deep Search Agents (2026.findings-acl)
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| Challenge: | Recent work emphasizes improving efficiency in LLM-based systems, especially for longcontext and multi-step reasoning. |
| Approach: | They analyze the role of listwise reranking in deep search pipelines and compare their results to a novel ETC metric to determine model scale and reasoning effort. |
| Outcome: | The proposed model scale, reasoning effort, reranking depth, and total token cost (ETC) metric improve retrieval and end-to-end accuracy and moderate reranked agents achieve comparable accuracy at substantially lower cost. |